Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
February 20, 2026ENGINEERING EnergyOpen Access

Artificial intelligence for energy materials research: From classical machine learning to large models

View Full Paper
Ask AI
Bookmark
Share

Authors

MJMingxi JiangJZJie ZhouYAYanggang An

Discussion

Loading...

Member takes

Overview

This review explores AI applications for enhancing energy materials efficiency, suggesting future pathways for innovation.

Key Points

  • The aim is to summarize the advancements in AI techniques for energy materials research, focusing on their impact on efficiency and innovation.
  • Systematically review AI methods from classical machine learning to advanced generative models
  • Discuss applications of graph neural networks and transformers for property prediction
  • Analyze the use of large language models and key databases in the field
  • Identify current challenges and future directions for AI in energy materials research
  • Advanced AI techniques improve efficiency and accuracy in predicting energy material properties
  • Generative models enable innovative designs for energy solutions like batteries and electrocatalysts
  • Challenges in AI include limited interpretability and underutilization of capabilities
  • Future work focuses on integrating multimodal language models to enhance research outputs

Cite This Study

Jiang et al. (2026) studied this question.

synapsesocial.com/papers/6997fa6dad1d9b11b34539b1https://doi.org/10.1007/s11708-026-1053-5
View Full Paper
Ask AI
Bookmark
Share